CLIP-Driven Prototype Network for Few-Shot Semantic Segmentation.

Recent research has shown that visual-text pretrained models perform well in traditional vision tasks. CLIP, as the most influential work, has garnered significant attention from researchers. Thanks to its excellent visual representation capabilities, many recent studies have used CLIP for pixel-level tasks. We explore the potential abilities of CLIP in the field of few-shot segmentation. The current mainstream approach is to utilize support and query features to generate class prototypes and then use the prototype features to match image features. We propose a new method that utilizes CLIP to extract text features for a specific class. These text features are then used as training samples to participate in the model's training process. The addition of text features enables model to extract features that contain richer semantic information, thus making it easier to capture potential class information. To better match the query image features, we also propose a new prototype generation method that incorporates multi-modal fusion features of text and images in the prototype generation process. Adaptive query prototypes were generated by combining foreground and background information from the images with the multi-modal support prototype, thereby allowing for a better matching of image features and improved segmentation accuracy. We provide a new perspective to the task of few-shot segmentation in multi-modal scenarios. Experiments demonstrate that our proposed method achieves excellent results on two common datasets, PASCAL-5i and COCO-20i.

An Opposition-Based Learning Black Hole Algorithm for Localization of Mobile Sensor Network.

The mobile node location method can find unknown nodes in real time and capture the movement trajectory of unknown nodes in time, which has attracted more and more attention from researchers. Due to their advantages of simplicity and efficiency, intelligent optimization algorithms are receiving increasing attention. Compared with other algorithms, the black hole algorithm has fewer parameters and a simple structure, which is more suitable for node location in wireless sensor networks. To address the problems of weak merit-seeking ability and slow convergence of the black hole algorithm, this paper proposed an opposition-based learning black hole (OBH) algorithm and utilized it to improve the accuracy of the mobile wireless sensor network (MWSN) localization. To verify the performance of the proposed algorithm, this paper tests it on the CEC2013 test function set. The results indicate that among the several algorithms tested, the OBH algorithm performed the best. In this paper, several optimization algorithms are applied to the Monte Carlo localization algorithm, and the experimental results show that the OBH algorithm can achieve the best optimization effect in advance.

Gaussian-Based Adaptive Fish Migration Optimization Applied to Optimization Localization Error of Mobile Sensor Networks.

Location information is the primary feature of wireless sensor networks, and it is more critical for Mobile Wireless Sensor Networks (MWSN) to monitor specific targets. How to improve the localization accuracy is a challenging problem for researchers. In this paper, the Gaussian probability distribution model is applied to randomize the individual during the migration of the Adaptive Fish Migration Optimization (AFMO) algorithm. The performance of the novel algorithm is verified by the CEC 2013 test suit, and the result is compared with other famous heuristic algorithms. Compared to other well-known heuristics, the new algorithm achieves the best results in almost 21 of all 28 test functions. In addition, the novel algorithm significantly reduces the localization error of MWSN, the simulation results show that the accuracy of the new algorithm is more than 5% higher than that of other heuristic algorithms in terms of mobile sensor node positioning, and more than 100% higher than that without the heuristic algorithm.

Ternary compound ontology matching for cognitive green computing.

Cognitive green computing (CGC) dedicates to study the designing, manufacturing, using and disposing of computers, servers and associated subsystems with minimal environmental damage. These solutions should provide efficient mechanisms for maximizing the efficiency of use of computing resources. Evolutionary algorithm (EA) is a well-known global search algorithm, which has been successfully used to solve various complex optimization problems. However, a run of population-based EA often requires huge memory consumption, which limited their applications in the memory-limited hardware. To overcome this drawback, in this work, we propose a compact EA (CEA) for the sake of CGC, whose compact encoding and evolving mechanism is able to significantly reduce the memory consumption. After that, we use it to address the ternary compound ontology matching problem. Six testing cases that consist of nine ontologies are used to test CEA's performance, and the experimental results show its effectiveness.

Efficacy and safety of mycophenolate mofetil in the treatment for IgA nephropathy: a meta-analysis of randomized controlled trials.

AIM: IgA nephropathy is virtually known as the most common glomerulopathy to end-stage renal failure in the world. Mycophenolate mofetil is a selective immunosuppressant widely used in organ transplantation, yet its tolerance and effectiveness in IgAN is controversial. METHODS: This is a systematic review and random-effects meta-analysis, searching PubMed, Embase, Te Cochrane Library, Science Citation Index, Ovid evidence-based medicine, Chinese Biomedical Literature and Chinese Science and Technology Periodicals. Screen out randomized controlled trials on patients with biopsy-proven IgA nephropathy and analysis mycophenolate mofetil treatment regimens used for therapy of IgA nephropathy. Complete remission and partial remission, doubling of creatinine level, proteinuria, incidence of end-stage kidney disease, infection, Cushing syndrome, diabetes, hepatic dysfunction or gastrointestinal symptoms, neurologic or visual ambiguity, acne, and alopecia were observed. RESULTS: Nine relevant trials were conducted with 587 patients enrolled. In Mycophenolate mofetil or plus medium/low-dose steroid comparing full-dose steroid alone or placebo, there was no significant difference. The risk of Cushing syndrome and diabetes had been significantly lowered with Mycophenolate mofetil-treated patients, while the risk of infection had been increased. CONCLUSIONS: Mycophenolate mofetil therapy did not differ in reducing proteinuria and Scr in patients with IgAN who had persistent proteinuria, while having fewer Cushing syndrome and diabetes risk and more infection risk. However, larger randomized studies are needed to reveal these results.

Two new phenylspirodrimanes from the deep-sea derived fungus Stachybotrys sp. MCCC 3A00409.

Two new phenylspirodrimanes, stachybotrin H (1) and stachybotrysin H (9) together with eleven known analogues (2-8, 10-13) were isolated from deep-sea derived Stachybotrys sp. MCCC 3A00409. Their structures were determined by extensive NMR data and mass spectroscopic analysis. Compounds 9-12 showed weak cytotoxicity against three human cancer cell lines K562, Hela and HL60 with IC50 in the range of 18.5-52.8 µM.

Four New Tirucallane Triterpenoids from the Fruits of Melia azedarach and Their Cytotoxic Activities.

Four new tirucallane triterpenoids, (21S,23R,24R)-21,23-epoxy-21,24-dihydroxy-25-methoxytirucall-7-en-3-one (2), (3S,21S,23R,24S)-21,23-epoxy-21,25-dimethoxytirucall-7-ene-3,24-diol (8), (21S,23R,24R)-21,23-epoxy-24-hydroxy-21-methoxytirucalla-7,25-dien-3-one (11), and (21S,23R,24R)-21,23-epoxy-21,24-dihydroxytirucalla-7,25-dien-3-one (12), along with 16 known analogues, 1, 3 - 7, 9 - 10, and 13 - 20, were isolated from the fruits of Melia azedarach. Their structures were elucidated by spectroscopic methods including 1D- and 2D-NMR techniques and mass spectrometry. These compounds were evaluated for their cytotoxicities against HepG2 (liver), SGC7901 (stomach), K562 (leukemia), and HL60 (leukemia) cancer cell lines. Compound 20 exhibited potent cytotoxicity against HepG2 and SGC7901 cancer cells with the IC50 values of 6.9 and 6.9 µm, respectively.

[Modeling of carbon dioxide measurement and optimization on building ceramic industry].

Input-output model and low carbon programming model on building ceramic industry were established. Carbon dioxide emissions of key steps and carbon footprint of products were calculated and predicted using the input-output model. While products planning was optimized using the low carbon programming model. The results showed that CO2 emission in the enterprise reached 182 543.9 t a year and CO2 emission per unit product was 10% more than advanced level in the world. 80% of the total CO2 was emitted during the processes of firing and drying. As a result, we should focus on these two steps in order to reduce carbon dioxide emission of building ceramic industry. Carbon footprint of blank tile, polished tile, and glazed tile were 150.2 t, 168.0 t, 159.6 t respectively. Optimized by the low carbon model, The ceramic enterprise could reduce carbon dioxide emission by 5.4% while not sacrificing any profit, and also could obtain profit 5.6% higher than before while unrise the carbon dioxide emission.

[Regulatory effect of Astragalus membranaceus on the immune disorder in rats with IgA nephropathy].

OBJECTIVE: To study the regulattory effect of Astragalus membranaceus on immune disturbance of the rats with IgA nephropathy. METHODS: Rats IgA nephropathy (IgAN) model was duplicated by oral feeding of bovine serum albumin (BSA), subcutaneous injection of carbon tetrachloride (CCl4) and injection of lipopolysaccharide (LSP) into vena caudalis. The rats were divided into three groups randomly for the normal, IgAN model group and the group treated with Astragalus membranaceus (treatment group). The treatment group was given the Astragalus membranaceus granules via intragastric administratsion, the normal group and the IgAN model group were given the equal amount of aqua destillata by gastric perfusion. The rats were examined for albuminuria, hematuria and pathological changes of renal tissue and the distribution of TGF-beta and interleukin-5 in renal tissue was determined by immunohistochemistry and the IFN-gamma and IL-4 of cytokine of Th1 and Th2 types were detected in rats IgA nephropathy model by sandwich enzyme linked immunosorbent assay (ELISA). RESULTS: (1) The hematuria in rats with IgA nephropathy significantly increased compared with normal control group and Astragalus treatment group (P < 0.05). There was significant increase in albuminuria in rats with IgA nephropathy, compared with normal control group and astragalus treatment group (P < 0.01). (2) The pathological change of glomerular mesangium, renal tubules and renal interstitia became serious in rats IgA nephropathy model when compared with normal control group and astragalus treatment group. Immumofluorescence showed renal IgA density in rats IgA nephropathy model was significantly higher than that in the normal control group (P < 0.001) and astragalus treatment group (P < 0.001). (3) The result of immuno histochemistry showed that there was only weak expression of TGF-beta and interleukin 5 in normal renal tissue. The expression of TGF-beta and interleukin 5 in IgA nephropathy model was significantly stronger than those in normal control group (P < 0.05) and astragalus treatment group (P < 0.05). (4) The serum IL-4 levels were (33.74 +/- 7.52) pg/ml in rats IgA nephropathy model, significantly higher than that in normal control group (2.36 +/- 0.85) pg/ml and astragalus treatment group (3.24 +/- 1.13) pg/ml. The IFN-gamma level in serum of rats IgA nephropathy model was (18.79 +/- 3.80) pg/ml, which was significantly higher than that in normal control group (46.53 +/- 5.56) pg/ml and astragalus treatment group (41.28 +/- 2.95) pg/ml. CONCLUSIONS: The astragalus could lower the level of hematuria and 24 hours-albuminuria of the IgAN model, and amelioratse the change of the renal pathology and reduce the deposit of IgA in glomerular mesangium. The possible mechanism of the effect is that astragalus could regulate the derangement of Th1, Th2, accordingly could improve the level of IL-4 and IFN-gamma in the serum and diminish the expression of cytokine Th2 TGF-beta1 and IL-5 of the renal tissue, and thereby could postpone the development of IgAN.